question_first experimentrun1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run1_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.3535248 0.07394484 -31.828115 2.644027e-222
## 2 mask_c 0.1141508 0.10022396 1.138957 2.547212e-01
## 3 feat_c 0.3238523 0.10022443 3.231271 1.232410e-03
## 4 mask_c:feat_c 0.4674695 0.20045201 2.332077 1.969664e-02
run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run2_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.58289325 0.07711001 -33.4962132 5.472142e-246
## 2 mask_c 0.07250391 0.10333206 0.7016594 4.828916e-01
## 3 feat_c 0.35802662 0.10333470 3.4647279 5.307682e-04
## 4 mask_c:feat_c -0.19824321 0.20665055 -0.9593161 3.373995e-01
run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run3_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.60639763 0.1039194 -25.0809650 8.024030e-139
## 2 mask_c 0.03532078 0.1052895 0.3354634 7.372756e-01
## 3 feat_c 0.22769008 0.1052929 2.1624440 3.058397e-02
## 4 mask_c:feat_c 0.04856399 0.2106582 0.2305345 8.176765e-01
feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = question_first)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.51772314 0.05241769 -48.0319405 0.000000e+00
## 2 mask_c 0.07415157 0.05923031 1.2519193 2.105993e-01
## 3 feat_c 0.30626015 0.05923297 5.1704338 2.335511e-07
## 4 mask_c:feat_c 0.11583961 0.11848037 0.9777114 3.282171e-01